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        <span>数据预处理</span>
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        <h1 id="数据预处理"><a href="#数据预处理" class="headerlink" title="数据预处理"></a>数据预处理</h1><p>对特征数据进行预处理，首先进行缺省值插补，然后再进行标准化</p>
<h2 id="缺省值插补"><a href="#缺省值插补" class="headerlink" title="缺省值插补"></a>缺省值插补</h2><p>首先将inf数据转换为nan</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 将inf数据转换为nan</span></span><br><span class="line">df = data.replace([np.inf, -np.inf], np.nan)</span><br></pre></td></tr></table></figure>

<p>pd直接将空格转换为nan所以只需要将nan转换为均值中值等就行</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 查看每一列缺失值的数量</span></span><br><span class="line">num = df.isnull().<span class="built_in">sum</span>()</span><br></pre></td></tr></table></figure>

<p>采用均值插补</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(num)):</span><br><span class="line">    <span class="keyword">if</span> num[i] &gt; <span class="number">0</span>:</span><br><span class="line">        <span class="comment"># 就需要进行均值填充</span></span><br><span class="line">        values = df[df.keys()[i]].mean()</span><br><span class="line">        df[df.keys()[i]].fillna(value=values, inplace=<span class="literal">True</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 查看每一列数据量和数据类型</span></span><br><span class="line"><span class="comment"># df.info()</span></span><br><span class="line"></span><br><span class="line"><span class="comment">#     def fillna(</span></span><br><span class="line"><span class="comment">#         self,</span></span><br><span class="line"><span class="comment">#         value=None,</span></span><br><span class="line"><span class="comment">#         method=None,</span></span><br><span class="line"><span class="comment">#         axis=None,</span></span><br><span class="line"><span class="comment">#         inplace=False,</span></span><br><span class="line"><span class="comment">#         limit=None,</span></span><br><span class="line"><span class="comment">#         downcast=None,</span></span><br><span class="line"><span class="comment">#         **kwargs</span></span><br><span class="line"><span class="comment">#     ):</span></span><br><span class="line"><span class="comment">#         return super().fillna(</span></span><br><span class="line"><span class="comment">#             value=value,</span></span><br><span class="line"><span class="comment">#             method=method,</span></span><br><span class="line"><span class="comment">#             axis=axis,</span></span><br><span class="line"><span class="comment">#             inplace=inplace,</span></span><br><span class="line"><span class="comment">#             limit=limit,</span></span><br><span class="line"><span class="comment">#             downcast=downcast,</span></span><br><span class="line"><span class="comment">#             **kwargs</span></span><br><span class="line"><span class="comment">#         )</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># https://blog.csdn.net/qq_43542339/article/details/105098235?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&amp;depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase</span></span><br><span class="line"><span class="comment"># df[&#x27;列名1&#x27;].fillna(value = 30，inplace=True)</span></span><br><span class="line"><span class="comment"># # value = 30，用30填补空值</span></span><br><span class="line"><span class="comment"># # value = df[&#x27;列名1&#x27;].mean() 均值填充</span></span><br><span class="line"><span class="comment"># # value = df[&#x27;列名1&#x27;].median() 中位数填充</span></span><br><span class="line"><span class="comment"># # value = df.Mer_min_distance.mode()[0]  众数填充</span></span><br><span class="line"><span class="comment"># df[&#x27;列名1&#x27;].fillna(method = &#x27;pad&#x27;,inplace=True)</span></span><br><span class="line"><span class="comment"># method参数取值：&#123;‘pad’, ‘ffill’,‘backfill’, ‘bfill’, None&#125;，使用过程中因为对ipad很熟悉，故常常用 &#x27;pad’填充</span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment"># ‘pad’ or ‘ffill’ : 用前一个非缺失值填充</span></span><br><span class="line"><span class="comment"># ‘backfill’ or ‘bfill’：用后一个非缺失值填充</span></span><br><span class="line"><span class="comment"># ‘None’ or default : 默认采用固定值填充</span></span><br></pre></td></tr></table></figure>



<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 将缺省值值替换为nan</span></span><br><span class="line"><span class="comment"># 转换和均值，需要一个数据来进行参考，没办法直接进行运行，所以还是采用fillna进行</span></span><br><span class="line"><span class="comment"># imp = SimpleImputer(missing_values=np.nan, strategy=&#x27;mean&#x27;)</span></span><br><span class="line"><span class="comment"># data = imp.fit(df)</span></span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br><span class="line">115</span><br><span class="line">116</span><br><span class="line">117</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/9</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">对inf,nan数据采用均值插补</span></span><br><span class="line"><span class="string">缺省值插补，然后再进行z-score</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="comment"># 缺失值插补</span></span><br><span class="line"><span class="comment"># from sklearn.impute import SimpleImputer</span></span><br><span class="line"><span class="keyword">from</span> sklearn <span class="keyword">import</span> preprocessing</span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line">feature = pd.read_excel(<span class="string">&#x27;F:/py/python-ECG信号处理/features_data&#x27;</span> + <span class="string">&#x27;/slp&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % <span class="number">1</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">data = pd.get_dummies(feature.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature), <span class="number">1</span>:])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 缺省值插补，采用均值插补</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">1.数据中有inf数据，将其转换为nan,然后用均值插补</span></span><br><span class="line"><span class="string">2.将nan和遗失数据用均值插补</span></span><br><span class="line"><span class="string"># 缺失值插补，用这个库</span></span><br><span class="line"><span class="string">from sklearn.impute import SimpleImputer</span></span><br><span class="line"><span class="string">SimpleImputer(add_indicator=False, copy=True, fill_value=None,</span></span><br><span class="line"><span class="string">              missing_values=nan, strategy=&#x27;mean&#x27;, verbose=0)</span></span><br><span class="line"><span class="string">              </span></span><br><span class="line"><span class="string">missing_values=nan可以用自己想要的填充  </span></span><br><span class="line"><span class="string">strategy=&#x27;mean&#x27;    缺省值插补填充的内容：mean  median, most_frequent</span></span><br><span class="line"><span class="string">数据是连续型，用均值填充</span></span><br><span class="line"><span class="string">数据是分类型，用纵数填充</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">利用ctrl+tab再点击函数查看函数</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">预处理方法：</span></span><br><span class="line"><span class="string">https://blog.csdn.net/Bryan__/article/details/51228971</span></span><br><span class="line"><span class="string">https://blog.csdn.net/luanpeng825485697/article/details/79845629?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&amp;depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="comment"># Series和DataFrame都会自动把None转换成NaN 然后 运算的时候会把NaN当成0,直接进行了填充，然后首先将inf转换为nan就行</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 将inf数据转换为nan</span></span><br><span class="line">df = data.replace([np.inf, -np.inf], np.nan)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 其他意见自动填充为nan，空格缺省直接填充nan</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 用fillna直接进行填充</span></span><br><span class="line"><span class="comment"># data.fillna()</span></span><br><span class="line"><span class="comment"># df[&#x27;列名1&#x27;].fillna(value = 30，inplace=True)</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># df.fillna(value=, axis=1)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看每一列缺失值的数量</span></span><br><span class="line">num = df.isnull().<span class="built_in">sum</span>()</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(num)):</span><br><span class="line">    <span class="keyword">if</span> num[i] &gt; <span class="number">0</span>:</span><br><span class="line">        <span class="comment"># 就需要进行均值填充</span></span><br><span class="line">        values = df[df.keys()[i]].mean()</span><br><span class="line">        df[df.keys()[i]].fillna(value=values, inplace=<span class="literal">True</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># z-score标准化</span></span><br><span class="line">std = preprocessing.scale(df)</span><br><span class="line">print(<span class="string">f&#x27;均值为：<span class="subst">&#123;std.mean()&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;标准差为：<span class="subst">&#123;std.std()&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;列均值为：<span class="subst">&#123;std.mean(axis=<span class="number">0</span>)&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;列标准差为：<span class="subst">&#123;std.std(axis=<span class="number">0</span>)&#125;</span>&#x27;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看每一列数据量和数据类型</span></span><br><span class="line"><span class="comment"># df.info()</span></span><br><span class="line"></span><br><span class="line"><span class="comment">#     def fillna(</span></span><br><span class="line"><span class="comment">#         self,</span></span><br><span class="line"><span class="comment">#         value=None,</span></span><br><span class="line"><span class="comment">#         method=None,</span></span><br><span class="line"><span class="comment">#         axis=None,</span></span><br><span class="line"><span class="comment">#         inplace=False,</span></span><br><span class="line"><span class="comment">#         limit=None,</span></span><br><span class="line"><span class="comment">#         downcast=None,</span></span><br><span class="line"><span class="comment">#         **kwargs</span></span><br><span class="line"><span class="comment">#     ):</span></span><br><span class="line"><span class="comment">#         return super().fillna(</span></span><br><span class="line"><span class="comment">#             value=value,</span></span><br><span class="line"><span class="comment">#             method=method,</span></span><br><span class="line"><span class="comment">#             axis=axis,</span></span><br><span class="line"><span class="comment">#             inplace=inplace,</span></span><br><span class="line"><span class="comment">#             limit=limit,</span></span><br><span class="line"><span class="comment">#             downcast=downcast,</span></span><br><span class="line"><span class="comment">#             **kwargs</span></span><br><span class="line"><span class="comment">#         )</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># https://blog.csdn.net/qq_43542339/article/details/105098235?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&amp;depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase</span></span><br><span class="line"><span class="comment"># df[&#x27;列名1&#x27;].fillna(value = 30，inplace=True)</span></span><br><span class="line"><span class="comment"># # value = 30，用30填补空值</span></span><br><span class="line"><span class="comment"># # value = df[&#x27;列名1&#x27;].mean() 均值填充</span></span><br><span class="line"><span class="comment"># # value = df[&#x27;列名1&#x27;].median() 中位数填充</span></span><br><span class="line"><span class="comment"># # value = df.Mer_min_distance.mode()[0]  众数填充</span></span><br><span class="line"><span class="comment"># df[&#x27;列名1&#x27;].fillna(method = &#x27;pad&#x27;,inplace=True)</span></span><br><span class="line"><span class="comment"># method参数取值：&#123;‘pad’, ‘ffill’,‘backfill’, ‘bfill’, None&#125;，使用过程中因为对ipad很熟悉，故常常用 &#x27;pad’填充</span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment"># ‘pad’ or ‘ffill’ : 用前一个非缺失值填充</span></span><br><span class="line"><span class="comment"># ‘backfill’ or ‘bfill’：用后一个非缺失值填充</span></span><br><span class="line"><span class="comment"># ‘None’ or default : 默认采用固定值填充</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 将缺省值值替换为nan</span></span><br><span class="line"><span class="comment"># 转换和均值，需要一个数据来进行参考，没办法直接进行运行，所以还是采用fillna进行</span></span><br><span class="line"><span class="comment"># imp = SimpleImputer(missing_values=np.nan, strategy=&#x27;mean&#x27;)</span></span><br><span class="line"><span class="comment"># data = imp.fit(df)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># # 正则化，标准化</span></span><br></pre></td></tr></table></figure>

<h2 id="标准化"><a href="#标准化" class="headerlink" title="标准化"></a>标准化</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># z-score标准化</span></span><br><span class="line">std = preprocessing.scale(df)</span><br><span class="line">print(<span class="string">f&#x27;均值为：<span class="subst">&#123;std.mean()&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;标准差为：<span class="subst">&#123;std.std()&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;列均值为：<span class="subst">&#123;std.mean(axis=<span class="number">0</span>)&#125;</span>&#x27;</span>)</span><br><span class="line">print(<span class="string">f&#x27;列标准差为：<span class="subst">&#123;std.std(axis=<span class="number">0</span>)&#125;</span>&#x27;</span>)</span><br></pre></td></tr></table></figure>

<h2 id="函数形式版"><a href="#函数形式版" class="headerlink" title="函数形式版"></a>函数形式版</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/9</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">均值插补缺省值</span></span><br><span class="line"><span class="string">z-score标准化</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn <span class="keyword">import</span> preprocessing</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">data_pre</span>(<span class="params">data</span>):</span></span><br><span class="line">    df = data.replace([np.inf, -np.inf], np.nan)</span><br><span class="line">    num = df.isnull().<span class="built_in">sum</span>()</span><br><span class="line">    [df[df.keys()[i]].fillna(value=df[df.keys()[i]].mean(), inplace=<span class="literal">True</span>) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(num)) <span class="keyword">if</span> num[i] &gt; <span class="number">0</span>]</span><br><span class="line">    df_scale = preprocessing.scale(df)</span><br><span class="line">    <span class="keyword">return</span> df_scale</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<p><img src="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1591682503590&di=e3acfbf739408b7c159ea65b9d13b665&imgtype=0&src=http://pic1.win4000.com/wallpaper/2018-05-30/5b0e3790a3f13.jpg"></p>

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